• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.445-460
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• 2011

The objective of this study was to construct and assess the applicability of the EFDC model for Saemangeum Reservoir as a 3D hydrodynamic and water quality modeling tool that is necessary for the effective management of water quality and establishment of conservation measures. The model grids for both reservoir system only and reservoir-ocean system were created using the most recent survey data to compare the effects of different downstream boundary conditions. The model was applied for the simulations of temperature, salinity, water quality variables including chemical oxygen demand (COD), chlorophyll-a (Chl-a), phosphorus and nitrogen species and algal biomass, and validated using the field data obtained in 2008. Although the model reasonably represented the temporal and spatial variations of the state variables in the reservoir with limited boundary forcing data, the salinity level was underestimated in the middle and upstream of the reservoir when the flow data were used at downstream boundaries; Sinsi and Garyuk Gates. In turn, the error caused to increase the bias of water quality simulations, and inaccurate simulation of density flow regime of river inflow during flood events. It is likely because of the loss of momentum of sea water intrusion at downstream boundaries. In contrast to flow boundary conditions, the mixing between sea water and freshwater was well reproduced when open water boundary condition was applied. Thus, it is required to improve the downstream boundary conditions that can accommodate the real operations of the sluice gates.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.870-877
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• 2020

A suction bucket foundation, especially useful at depths of more than 20m, is a method of construction. The method first places an empty upturned bucket at the target site. Then, the bucket is installed by sucking water or air into it to create negative pressure. For stability, it is crucial to secure the verticality of the bucket. However, inclination by the bucket may occur due to sea-bottom conditions. In general, a repeated intrusion-pulling method is used for securing verticality. However, it takes a long time to complete the job. In this paper, we propose a real-time suction bucket verticality monitoring system. Specifically, the system consists of a sensor unit that collects raw verticality data, a controller that processes the data and wirelessly transmits the information, and a display unit that shows verticality information of a circular steel pipe. The system is implemented using an inclination sensor and an embedded controller. Experimental results show that the proposed system can efficiently measure roll/pitch information with a 0.028% margin of error. Furthermore, we show that the system properly operates in a suction bucket-based model experiment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.65-75
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• 2017

The Detection Model is the model to find the result of a certain purpose using artificial intelligent, data mining, intelligent algorithms In Cyber Security, it usually uses to detect intrusion, malwares, cyber incident, and attacks etc. There are an amount of unlabeled data that are collected in a real environment such as security data. Since the most of data are not defined the class labels, it is difficult to know type of data. Therefore, the label determination process is required to detect and analysis with accuracy. In this paper, we proposed a KDFL(K-means and D-S Fusion based Labeling) method using D-S inference and k-means(unsupervised) algorithms to decide label of data records by fusion, and a detection model architecture using a proposed labeling method. A proposed method has shown better performance on detection rate, accuracy, F1-measure index than other methods. In addition, since it has shown the improved results in error rate, we have verified good performance of our proposed method.

• Journal of Korea Water Resources Association
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• v.49 no.1
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• pp.61-72
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• 2016

In Korea, the aquifers at the coastal areas are mostly shallow alluvial unconfined aquifers. To simulate the flow and dispersion in unconfined aquifer, a FDM model has been developed to solve the nonlinear Boussinesq equation. Related analysis and verification have been executed. The iteration method is used to solve the nonlinearity, and the model shows 3-D shape because it is a 2-D y model that consider the undulation of water table and bottom. For the verification of the model, the output of flow module is compared to the 1-D analytic solution of Lee (1989) which have the drawdown or uplift boundary condition, and the two results show almost the same value. and the mass balance of dispersion module shows about 10% error. The developed model can be used for the analysis and design of the flow and dispersion in the unconfined aquifers. The model has been applied to the estuary area of Ssangcheon watershed, and the parameters have been deduced as a result : hydraulic conductivity is 90 m/day, and longitudinal dispersivity is 15 m. And the analysis with these parameters shows that the wells are situated in the influence circle of each others except for No. 7 well. Groundwater discharge to sea is $3700m^3/day$. And the chlorine ion ($cl^-$) concentration at the pumping wells increase at least 1000 mg/L if groundwater dam is not exist, so the groundwater dam plays an important role for the prevention of sea water intrusion.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.161-168
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• 2007

Web attack uses structural, logical and coding error or web application rather than vulnerability to Web server itself. According to the Open Web Application Security Project (OWASP) published about ten types of the web application vulnerability to show the causes of hacking, the risk of hacking and the severity of damage are well known. The detection ability and response is important to deal with web hacking. Filtering methods like pattern matching and code modification are used for defense but these methods can not detect new types of attacks. Also though the security unit product like IDS or web application firewall can be used, these require a lot of money and efforts to operate and maintain, and security unit product is likely to generate false positive detection. In this research profiling method that attracts the structure of web application and the attributes of input parameters such as types and length is used, and by installing structural database of web application in advance it is possible that the lack of the validation of user input value check and the verification and attack detection is solved through using profiling identifier of database against illegal request. Integral security management system has been used in most institutes. Therefore even if additional unit security product is not applied, attacks against the web application will be able to be detected by showing the model, which the security monitoring log gathering agent of the integral security management system and the function of the detection of web application attack are combined.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.